and production. The importance of ERP systems hasbeen increasingly recognized by organizations of all kinds. Nevertheless the implementation of such systems has provedto be difficult, in that it demands considerable resources for long periods of time. This study has several goals: (1) reviewthe literature on information systems acceptance models in terms of prospective individual adoption, (2) empiricallycompare eight prominent models and their extensions to ERP systems, (3) examine the relationships among fundamentalconstructors, (4) examine the effect of moderators on these relationships including age, gender and experience and (5)formulate a model that integrates elements across these eight models and best describes the acceptance of ERP systems.

Organizations consider ERP to be its backbone and a vital tool for organizational excellence because it integratesvaried organizational systems, and enables flawless transactions and production (Al-Mashari et al. 2003,Koh et al.2008,Parthasarathy

et al. 2007).An ERP system can reduce costs, and thus lead to greater effectiveness and a bettercompetitive edge in terms of improved strategic initiatives and responsiveness to customers (O'Leary 2000, Sandoe et al.2001, Rashid et al. 2002, Bharadwaj et al. 2007, Ge & VoB 2009). Enterprise system software constitutes a multi-billiondollar industry that produces components to support a variety of business functions (Chellappa&

Saraf 2010). ITinvestments have grown to be the largest category of capital expenditures in United States-based businesses over the pastdecade (Ranganathan&

Brown 2006). Implementing an ERP system is different from implementing a traditionalsoftware development system since it is not “built to order” but rather bought “as is”, and is transaction driven rather thanprocess-centric in its focus, with different levels of adaptability (Basu&Kumar 2002). Although ERP has been depictedas a panacea in both the literature and in practice, there are many reports of difficulties in implementing ERP systems

(Ram et al. 2013). Chang (2004) reported that (a) 90% of ERP implementations are delivered late or are over-budget,(b) enterprise initiatives show a 67% fail rate in achieving corporate goals and outcomes are considered negative orunsuccessful, (c) more than 40% of all large-scale projects fail. Furthermore, ERP projects also fail because of errors inmanaging leadership (42%), organizational and cultural (27%), human and people (23%), technology and otherdimensions (8%) (Waters 2006).

This study has several goals: (1) review the literature on information systems acceptance models in terms ofprospective individual adoption, (2) empirically compare eight prominent models and their extensions in the field of ERPsystems (Table 1), (3) examine the relationships among fundamental constructors, (4) examine the effect of moderatorson these relationships including age, gender and experience and (5) formulate a model that integrates elements acrossthese eight models that best captures the steps toward acceptance of ERP systems.

#

Model

Source

1

TAM-

Technology acceptance model

Davis, 1989

2

TAM2-

a revised model of TAM

Venkatesh & Davis, 2000

3

UTAUT-

Unified theory of acceptance and use of technology

Venkatesh et al., 2003

4

TTF-

Task technology fit model

Goodhue & Thompson, 1995

5

TAM+TTF–

愠combin敤 mod敬

䑩ahaw C p瑲ongI 1999

S

䑏aJ†䑩afus楯n of fnnov慴楯n mod敬

䵯or攠C B敮b慳慴Ⱐ 1991

Table 1–

eight prominent technology acceptance models

2.

Literature review

Along with increasing investments in new technologies, their acceptance has become a frequently studied topic in thefield of information systems. In the last two decades acceptance models have been proposed, tested, refined, extendedand unified. Previous

studies have presented a variety of theoretical models to support successful ERP adoption andimplementation (Calisir

&Calisir, 2004). Studies on acceptance in the field of information systems reflect twomainstreams of research (Venkatesh et al. 2003).

Each of these which has made an important and unique contribution tothe literature, although as noted by Lin et al. (2007) most empirical studies of technology acceptance models have beenlimited to the technology acceptance-related issues of individual users.

One stream examines the individual psychological characteristics that influence technology acceptance, and useintention or usage as a dependent variable (Compeau

&Higgins 1995b; Davis et al. 1989). This type of approach is validfor almost any

technology. Although developed within the IS field, it nevertheless does not consider the specificcharacteristics of software and makes no distinction between software, hardware and services of the IT departments(Delone

& McLean

2003). Thus although

the

individual

perceptions are

differentiated the

technology

is

black-

boxed

and

no

specific

features,

tools

and

mechanisms

are included (Bhattacherjee

&Sanford 2006).

The secondstream examines implementation success through the fit of the

technology either overall in terms of its technologicalcharacteristics or at the organizational level (Goodhue

&Thompson 1995, Autry et al. 2010). This stream explicitlyconsiders the attributes of information and systems which produce information such

as data

quality,

ability

to

retrieve

and

consolidate

required

data

and reliability (Moore

&Benbasat 1991, Delone &McLean

1992, 2003).

Among the theoretical models within the first stream, the technology acceptance model (TAM) developed by Davis(1989) appears to be the most widely used by technology researchers and managers becauseof its empirical support (Leeet al.2009). The TAM model draws on the theory of reasoned action (TRA) developed by Fishbein and

Ajzen (1975)

and is based on the hypothesis that technology acceptance and use can be explained in terms of the individual's internaland perceived beliefs of technology usefulness, ease of use and intentions (Davis 1989). The TAM model can be appliedto predict future technology use by examining data from the time that the technology was introduced. The TAM hasgiven rise to two subsequent models. TAM2, developed by Venkatesh&Davis (2000) preserves the core philosophy ofthe model but incorporates additional theoretical constructs spanning social influence processes to reflect the impact onan individual deciding to adopt or reject a new system. The UTAUT model refines how the determinants of intention andbehavior evolve over time and emphasizes that most of the key relationships in themodel are moderated (e.g. age,gender, experience) to respond to the interest in workplace environments to create equitable settings for women and menof all ages (Venkatesh et al. 2003).

Compeau&Higgins (1995b) extended one of the most influential theories of human behavior, Social CognitiveTheory (SCT) to the context of technology utilization. SCT, developed by Bandura (1986) defines human behavior as aninteraction of personal factors, behavior

and the environment. SCT posits that learning will most likely occur if there is aclose identification between the observer and the model (i.e. the individual who is imitated) and if the observer also has agood deal of self-efficacy. Bandura (1986) argued that an individual's self-efficacy beliefs affect behavior and function asan important set of proximal determinants of human motivation and action which operate on action through affectiveintervening processes. These include motivational process (people are more likely to expend more effort and persist7

CSE-

Computer self-

efficacy model

Compeau & Higgins 1995

8

D&M

-

䑥汯n攠and 䵣L敡n I匠獵捣ess mod敬

䑥汯n攠 & 䵣L敡n, 2003

longer in a task) and cognitive process (people are more likely to take a wider picture of a task and be encouraged byobstacles to greater effort when performing the task).

Several models draw on constructs from both streams of research. Diffusion of Innovation (DOI) theory viewsinnovation as communicated through certain channels over time and within a particular social system (Rogers, 1995).The rate of adoption of innovations is influenced by five factors: relative advantage (i.e. usefulness), complexity (i.e. easeof use), compatibility, trainability and observability (Rogers, 1995).

Moore&Benbasat (1991), working in an IS context,expanded on the Rogers' factors to generate eight factors: voluntariness, relative advantage, compatibility, image, ease ofuse, result demonstrability, visibility and trialability which all impact the adoption of IT.

Since the early applications ofDOI to IS research, the theory has been applied and adapted in numerous ways.

However, research has consistentlyfound that technical compatibility, technical complexity, and relative advantage are important antecedents to the adoptionof innovations (Bradford&Florin, 2003; Crum et. al., 1996) all of which have led

to a generalized and simpler model.

Dishaw&Strong (1998) adapted key models of information technology (IT) utilization behavior from the MISliterature (TAM and TTF models) to suggest a combined model that delivers more explanatory power than either modelalone. The result is an extension of TAM to include a Task-technology fit (TTF) construct. Models that integrateconstructs from both streams of research have greater explanatory power. They argued that research using the integratedmodels should lead to a better understanding of choices concerning the use of IT. Each of these combined modelsprovides a much needed theoretical basis for exploring the factors that explain software utilization and its links with userperformance.

Delone& McLean

(1992) defined four antecedents of user acceptance and organizational benefits: system quality,information quality, user satisfaction and user intention to use the technology. DeLone& McLean

(2003) suggested thatuse and intention to use are alternatives in their model,

and that intention to use may be worthwhile in the context ofmandatory usage such as ERP systems. Most researchers agree with DeLone &McLean’s (2003) argument that servicequality, when properly measured, should be added to system quality and information quality as predictors of usersatisfaction and user intention to use the technology (Wang&Liao 2006).

These models have contributed to our understanding of user technology acceptance factors and their relationships.The acceptance models in the field of information systems are based on different (and partially overlapping) sets ofdependent and independent constructs. Nevertheless they also present two limitations: their relatively low explanatorypower and inconsistent influences of the factors acrossstudies (Sun&Zhang 2006).

3.

Hypotheses

Beyond the empirical comparison of these known acceptance models as described above, this research also aims toexplore the effect of key individual user differences on the main relationships among core constructs.Agarwal& Prasad(1999)

explored the effect of individual user differences on technology acceptance. They found that each of thesemoderators was fully mediated by core constructs, implying that simpler models could be constructed that excludeindividual

1995a). Burton-Jones&Hubona (2006) found consistent proof of relationships betweenusers’ characteristics and IT in the literature.

They argued that there are several justifications for key individual userdifferences including the fact that older users tend to resist change and may be less able to appreciate or understand it.They therefore perceive new IT as less useful, and find it more difficult to learn and use unfamiliar technology even ifthey are willing to adopt a new IT. In addition, these authors' view most user behavior as non-cognitive and claim thatcore constructs cannot fully mediate individual differences associated with user habits.

Several key individual user differences have been found to be significant in acceptance models in the context ofinformation systems. This study incorporates: age, gender and experience, the three best documented individual userdifferences to examine the key relationships among fundamental constructors system in both mandatory and voluntarysettings (Yi et

al. 2006, Burton-Jones & Hubona

2005, Morris& Venkatesh

2000). It deliberately neglects otherindividual user differences because of either irrelevance to the field of ERP systems (i.e. voluntariness, since ERP isperceived to be associated with mandatory usage) or inconsistent findings the field of information systems (i.e. level ofeducation).

Most of the models investigated in this study aim to measure potential user's attitudes toward adopting an informationtechnology (Moore&Benbasat 1991, Davis 1989, Venkatesh&

Davis 2000, Dishaw&Strong 1998, Venkatesh et al.2003). Therefore, intention to use an information technology is a prominent dependent variable in most models.However, the CSE model developed by Compeau&Higgins (1995b), used actual usage as a dependent variable. Herewe examine the predictive validity of all models in the context of intention to enable a comparison of the models.However, the intention construct in many technology acceptance studies has been measured via voluntary orientedstatements of usage such as "I intend", "I plan'' or "I predict". Nah et al. (2004) claimed that these measures areinappropriate to assess acceptance of mandatory technologies such as ERP systems.Chang et al. (2008) argue thatalthough the use ofERP systems

may not be voluntary, the understanding of system adoption from the user’s perspectiveis useful in helping

the organizations prepare their employees to face new challenges and learn how to make good use ofthe

technology.Seymour et al. (2007)

suggested that this dependent variable should be redubbed the 'symbolic adoption'variable, to describe potential adopters' mental acceptance of mandatory information technology in a better way. Basedon these the models and literature review, a number of

hypotheses were formulated to identify antecedents of symbolicadoption (Table 2). These hypotheses are refined to include the moderating variables that have been acknowledged ashaving an effect on the relationships between the independent variables andsymbolic adoption.

4.

Research methodology

4.1.

Data Collection

The authors developed eight structured questionnaires, one for each model. The instruments were adapted frommeasures developed throughout the model development and from instruments validated inprevious quantitative studiesof a similar nature as listed in Table 3.

#

Model

Source for validated instruments

1

TAM

Davis 1989; Davis et al. 1989

2

TAM2

Venkatesh & Davis 2000

3

UTAUT

Venkatesh et al. 2003

4

TTF

Goodhue & Thompson 1995

5

TAM+TTF

Dishaw & Strong 1999, Goodhue & Thompson 1995

6

DOI

Moore & Benbasat 1991

7

CSE

Compeau&

Higgins 1995b, Compeau et al. 1999

8

D&M

Delone&

McLean 2003, Iivari 2005, Ifinedo&Nahar 2007

Table 3-

Source for validated instruments

Eachquestionnaire consisted of two components. The first component was demographic questions about therespondents and the extent to which they used the ERP system. This questionnaire was administered at a certain point intime and therefore a question on prior experience in ERP systems was added to enable an analysis of the impact ofexperience on adoption. The second component consisted of the items measuring the core constructs that were defined inthe models. A five point Likert-type scale was used where 1=strongly disagree to 5=strongly agree. The fullquestionnaires are not shown due to space constraints.

Each questionnaire was referred by approximately 100respondents. The questionnaires were mailed, from September 2010 to December 2011 and returned by approximately800 respondents (eight questionnaires in overall-

one for each model) in the Mediterranean region working in SMEs inwhich an ERP system was implemented.

Several constructs are common across models. For example, previous studies have indicated that performanceexpectancy (defined in the UTAUT and CSE model) and relative advantage (defined in the DOI model) constructs aresimilar (Compeau&Higgins 1995b,

Davis et al. 1989, Moore& Benbasat 1991,Plouffe et al. 2001, Venkatesh et al.2003). Therefore, to enhance the explanatory power of the following analyses, constructs that were common acrossmodels were measured in the same manner to enlarge the data sample. Thus, for example, the analysis of the TAM modelthat was returned by approximately 100 respondents could be measured on a sample size of approximately 500respondents because the 'perceived usefulness' and 'perceive ease of use' constructs are common across five models(TAM, TAM2, UTAUT, DOI and TAM+TTF).

4.2.

Reliability analysis

A reliability analysis determines the extent to which the measurements resulting from an analysis are the result ofcharacteristics of the features being measured. A reliability analysis also evaluates the internal consistency of themeasurement items grouped under the core constructs in the models. In most cases and in this research as well, theavailable variables were only the observed variables and therefore this method is purely theoretical. As a result, we usedan internal consistency method that is closely associated with reliability analysis and enables an empirical analysis ofmeasurement reliability.

Internal consistency was measured by Cronbach’s Alpha. High communality values for all sub factors indicate thatthe total amount of variance that an

original factor shares with all other factors is high. Hair et al. (1995) indicated thatthe lowest acceptable value ranges between 0.60 and 0.70 whereas Nunnally (1978) and Fornell&Larcker (1981)recommended a Cronbach's Alpha limit of 0.70 for reasonably high reliability.

The measurement model estimations for the models, based on the internal consistency reliability (ICR) analysis,showed similar internal consistency values, means and standard deviations for both the entire questionnaire and the set ofreduced measurement items. In addition, the square roots of the shared variance between the constructs and theirmeasurement items were higher than the correlations across constructs, supporting convergent and discriminant validity.The results of the measurement model estimations for both cases are not shown here due to space considerations.

or internal consistency scores, excessive multicollinearity in formative constructs can destabilizethe model. If measures are highly correlated, it may suggest that multiple indicators are tapping into the same aspect ofthe construct (Diamantopoulos&Siguaw 2006). Therefore, to ensure that multicollinearity was not present,multicollinearity analysis was performed using the variance inflation factor statistic (VIF). Although general statisticstheory posits that multicollinearity occurs if the VIF value

is higher than 10, the authors tested multicollinearity for astrict VIF threshold of 3.3 out of model destabilization considerations (Diamantopoulos&Siguaw 2006).

4.4.

Hierarchical regression

Cronbach (1987) suggests that interaction effects should be evaluated by stepwise hierarchical regression. Prior to thehierarchical regression an additive transformation on the predictor variables should be performed. The transformation fora given predictor involves subtracting the mean of the predictor variable fromeach individual's raw score on thatpredictor, thus forming deviation scores. To eliminate the effect of multicollinearity of variables, the interaction termwas formed by multiplying the two centered variables together (Aiken&West 1991). Thus, such a transformation willyield low correlations between the product term and the component parts of the term. This is desirable, because itdecreases the probability of computational errors (Jaccard et al. 1990).

In the first step, we entered the independentvariables into the regression model to verify the main effects of the independent variables. Then, in a separate step, theproduct of the independent variables, which represents the moderator effect, was entered. This stepwise hierarchicalapproach providesan unambiguous test of moderator effects (Aiken&West 1991). Furthermore, to determine the natureof this interaction, we performed a simple slopes analysis (Aiken & West 1991).

The variance explained by the models, without the inclusion of the moderating variables, was relatively modest, aspresented in Table 12. In addition, the variance explained by the models after the inclusion of the moderating variablesincreased across all models. However, the variance explained by the models, in the field of ERP systems, in an absolutemanner, even after the inclusion of moderating variables, increased only slightly and at best only accounts for 41% of thevariance. The models show a 29% increase in explained variance (on average) whereas the CSE model shows thehighest percentage of increase in explained variance after including the moderating variables (45%) but neverthelessshows the least explained variance in both cases (before and after the inclusion of moderating variables-

15% and 21%respectively). The D&M model does not include the influence of any moderating variables and therefore was analyzedfor the influence of core

constructs alone.

Model

Before

After

% change

Model

Before

After

% change

1

TAM

0.24

0.31

+28%

5

TTF

0.20

0.23

+13%

2

TAM2

0.25

0.35

+37%

6

CSE

0.15

0.21

+45%

3

UTAUT

0.29

0.37

+27%

7

TAM+TTF

0.31

0.39

+26%

4

DOI

0.32

0.41

+29%

Table 4-

Variance explained by the models before and after including moderating variables

With regard to TAM model and its extensions (i.e. TAM2 and UTAUT) the findings indicate that newer versionsincreased the amount of explained variance of the previous modelboth before including the moderating variables (i.e.TAM explains 24%, TAM2: 25% and UTAUT: 29%) and after (i.e. TAM explains 31%, TAM2: 35% and UTAUT:37%). In addition, three models-

DOI, the combined model (TAM+TTF) and UTAUT model-

showed the highest

explained variance in both cases. These three models, in contrast to the other models, are not focused solely on theindividual perspective but include organizational and management dimensions in addition to the individual dimensions.Brown et al. (2002)found that using TAM to evaluate ERP acceptance provided a limited explanation of end-users’behavior, attitudes and perceptions towards the system, and thus delivers misleading recommendations for organizations.

In addition, UTAUT is considered an improvement over the TAM extension models when evaluating end-useracceptance of ERP systems because it makes it possible to consider the mandatory nature of ERP systems. An implicitassumption of earlier technology acceptance models (i.e. TAM, TAM2) is that users of the information systems havesome level of choice with regard to the extent that they use the technology (Amaoko-Gyampah & Salam, 2004, Nah et al.

2004). Furthermore, the UTAUT model incorporates a facilitating conditions construct which is defined as the objectivefactors, such as the provision of support for users, in the environment that makes an application easy to use. The DOImodel is based on a diffusion process developed by Rogers (1962) which is defined as a communicative process ratherthanan individually focused process. Thus, the DOI model introduces variables related to the organizational aspects suchas result demonstrability, trialability and visibility within the organization. In this sense, the DOI model is considered an

It is important to emphasize that most of the key relationships in the models were moderated. Gender, which hasreceived more attention in the literature, was found to be a keymoderating influence. User prior experience in complexIT settings, such as ERP systems, was the second key moderating variable. According to Venkatesh et al. (2003) anothermoderating variable, age, has received little attention in the technology acceptance research literature. Our findingsindicate that in the context of complex IT settings, age emerges as an important moderator of key relationships in themodels.

Hypothesis

Result

Hypothesis

Result

Hypothesis

Result

H1a

Medium Support (4 of 7positive)

H4a

Supported

H9

Supported

H1b

Week Support (1 of 7 positive)

H4b

Supported

H10

Supported

H1c

Strong Support (7 of 7 positive)

H5a

Supported

H11

Not Supported

H2a

Strong Support (5 of 5 positive)

H5b

Supported

H12

Supported

H2b

StrongSupport (5 of 5 positive)

H6

Supported

H13

Supported

H3a

Strong Support (3 of 4 positive)

H7

Supported

H3b

Strong Support (4 of 4 positive)

H8

Supported

Table13-

Hypotheses results

The perceived usefulness, performance expectancy, relative advantage and task-technology-fit constructs wereacknowledged by previous studies as similar (Calisir et al. 2009, Venkatesh et al. 2003). These constructs, in this study,were not found to be significant within all models. This finding corroborates a few studies in the field of ERP (Seymouret al. 2007) but is inconsistent with most general information systems acceptance research. This result is nevertheless isvery significant in that it shows that in a complex technology implementation environment such as ERP implementation,unlike less complex environments, the perceived usefulness of the technology is perhaps less important than its ease ofuse. Many organizations are committed to a “vanilla”implementation to avoid ERP software modifications and businessprocess re-engineering in particular to align best business standards for a successful ERP implementation (Al-Mudimigh2007, Finney&Corbett 2007, El-Sawah et al. 2008). Consequently, potential adopters are less troubled by how toexecute old processes in the new system because of the obligation to run new business processes based on best practicethat are already well implemented in the ERP system with minimal changes needed. Thus, managerial

attempts that havefocused on enhancing the perceived usefulness of the ERP system will be less worthwhile than the managerial attemptsfocused on enhancing the perceived ease of use.

In addition, in cases where these similar constructs were found to besignificant, they were not found to be the strongest predictor of user symbolic adoption by contrast to several studies.These results perhaps suggest that perceived usefulness has lower explanatory power in comparison to other constructs inthe context of complex IT settings.

Contrary to predictions and in contrast to previous studies, the results indicate, that the influence ofusefulnessconstructson symbolic adoption was not moderated by age or gender. Venkatesh et al. (2003) posited that since mentendto be highly task-oriented, performance expectancy centered on task accomplishment is likely to be especially importantto men because of socialization processes. In addition, they argued that research on age differences indicates that youngerusers may place more importance on extrinsic rewards. However, in the case of ERP systems the latter may be perceivedas rich in functionality and beyond the needs of the reasonable user (Yi

et al.2006). Therefore, users' main concern maybe the extent to whichthe ERP system is easy to use rather than the extent to which the system is useful. Thus, thepresent study reveals that age and gender differences do not play a role in ERPs contexts with regard to the perceivedusefulness construct.

Another frequent hypothesis concerns the potential moderating effect of experience. According to Castaneda et al.(2007) user beliefs are the key perceptions driving IT usage and may change with time as users gain experience. It wasfound that the effect of perceived usefulness on user symbolic adoption increases with increasing experience. Oneexplanation may be related to training programs. Users' training is important not only for acquiring skills but also enablesadjustment to changes created by the implementation of an ERP system and allows potential adopters to get firsthandexperience and explore the ERP system (Amoako-Gyampah&Salam, 2004, Aldwani 2001, Brown et al. 2002).Experienced users evaluate a system in a more in-depth way and hence may consider perceived usefulness to a greaterextent than inexperienced ones (Jasperson et al. 2005).

In this study, and consistent with most previous studies, perceived ease of use, as formulated by different constructs(e.g. effort expectancy), was found to be a significant predictor of user symbolic adoption. Furthermore, in the context ofmoderating factors, and consistent with previous research (e.g., Agarwal&Prasad 1997, 1998; Davis et al. 1989;Thompson et al. 1991, 1994, Morris&Venkatesh 2000), less experienced youngerwoman ascribed more importance toease of use aspects than men, as they tend to gain efficacy over time. Age differences have been associated with growingdifficulty in processing complex stimuli and allocating attention to information on the job (Venkatesh et al .2003). Scott&Walczak (2009) suggested that ERP users in organizations with diverse ages often find ERP training challenging,despite their work experience. In addition, it was found that women may place more importance on ease of use aspectsthan men because of individual perceptions related to gender roles. Thus, age, gender and experience differences exist inthe context of ERPs.

Consistent with most previous studies in mandatory settings, the results showed for all models that the socialinfluence construct is a significant predictor of symbolic adoption. In addition and in line with previous research, thesocial influence effect on symbolic adoption of ERP system was moderated by: 1) age because affiliation requirementsincrease with age, 2) gender because women tend to be more sensitive to others’ opinions and 3) experience, inmandatory settings, because in the early stages of individual experience social issues impact the technology and its rolesbut eroding over time and eventually become non-significant with sustained usage (Venkatesh&

Davis 2000, Morris&Venkatesh 2000, Venkatesh et al. 2003). Thus, these moderating variables simultaneously influence the social influence-intention relationship not only in a simple technology environment but in a complex technology environment as well.

The facilitating conditions construct, in the context of information systems, is associated with the provision of ITsupport. Venkatesh (2000) argued that effort expectancy fully mediates the effect of

facilitating conditions on intentionbecause facilitating condition issues (e.g. support) are largely captured within the effort expectancy construct which tapsthe ease with which that tool can be applied. Thus in the context of complex IT settings, such

as an ERP system, theseconstructs may not share similar themes since the support given to users may not be good enough to satisfy users anddeliver an ease of use experience. The current results show that in complex IT settings such as an ERP system, thisconstruct is not fully mediated by effort expectancy and influences symbolic adoption considerably. In addition andconsistent with previous studies, this study shows that the effect of facilitating conditions on symbolic adoption increaseswith experience in that users gradually find multiple avenues for help and support. Age also has an effect since olderusers attach more importance to receiving help and assistance on the job which is more strongly emphasized in thecontext of a complex IT becauseof the increasing cognitive and physical limitations associated with age (Morris&Venkatesh 2000, Venkatesh et al. 2003).

female gap in computer anxiety, which initially showed women to be more anxious, is slightlydeclining but still persists in the USA.In addition, although affect was found to be a significant determinant of usersymbolic adoption, previous research has shown that affect, associated with intention to use, is fully mediated byperformance and effort expectancy (Venkatesh et al. 2003).

Since the early applications of DOI to IS research, this theory has been applied and adapted in numerous ways.

Severalstudies defined compatibility as the extent to which the innovation is perceived to be consistent with the potentialadopters' existing

values, previous experience and needs. Other studies defined it in terms of technical compatibility withregard solely to hardware and software issues (Bradford&Florin 2003). Nevertheless, studies have consistently foundthat technical compatibility isan important antecedent to the adoption of innovations (Bradford&Florin, 2003).However, in terms of ERP packages, compatibility, from a standards perspective, may be broader.

Iivari (2005) found that system quality emerged as more significant than information quality, presumably because ofthe mandatory nature of analyzing the system for acceptance. The present study is consistent with Iivari's (2005) study.Since an ERP system is used on a daily basis in organizations, it is natural that the information

output is timely.However, Zhang et al. (2004) argued that the variables of information quality and system quality from the D&M modelshould be modified to take the specific conditions of a large mature off-

the-

shelf ERP package into account. First, intheenvironment of an ERP system, the integrity of raw input data affects others users who operates the different modules.Second, ERP system packages have been developed for many years and used in many sites, which enables the packagesto be very mature and reliable. In addition, this study showed that service quality is a significant predictor of symbolicadoption.

5.1.

Enterprise resource planning acceptance model

A major paradigm in psychology and marketing argues that affect (defined as an umbrella for a set of more specificmental processes including emotions, moods, and attitudes) and cognition (referring to more specific mental processesare separate and partially independent systems (Zajonc, 1984). Most models or theories in IS focus on the cognitive and

behavioral aspects of human decision-making processes and on individual reactions to using technologies inorganizations (Sun&Zhang 2006).

The basic idea in the model proposed below is that a user's symbolic adoption of an information system incomplexIT settings is influenced by cognitive reactions and technical features that are considered separate and partiallyindependent systems. The hypothesis is that these two components together determine the user's final symbolic adoption.

We drew on the analysis above to identify several key constructs and key moderators to make up the main dimensionsof the model (see Figure 9). The model is based on the incorporation of the main constructs defined in previous researchin the field of information systems that are thought to be significant in the field of ERP systems, as described in Table 13.

With regard to ERP systems we assumed that the facilitating condition construct is very similar to the service qualityconstruct in terms of the extent to which an

individual believes that an organizational and technical infrastructure existsto support use of the system. In addition, task-technology-fit and compatibility are very similar constructs. Thecompatibility construct incorporates items that tap the fit between all aspects of an individual’s work and the use of thesystem in the organization (Venkatesh et al. 2003). These aspects are covered by three constructs in the new model: 1)perceived usefulness, defined by the degree to which a person believes thatusing an IS system will enhance his jobperformance, 2) level of integration, which influences job performance beyond users' initial perception and 3) offset fromstandard, which can increase job performance, and its counterpart, hazard system quality.

In this study, as in previous work, the CSE model was analyzed for the effect of these constructs on users' willingnessto use the system (dropping the ease of use construct). According to Venkatesh et al. (2003) self-efficacy and anxiety aretheorized not

to be direct determinants of intention. Previous research has shown that self-efficacy and anxiety areconceptually and empirically distinct from perceived ease of use and yet are fully mediated by perceived ease of use inexplaining intention to use andthus were modeled as indirect determinants of user symbolic adoption. Therefore, thesuggested model ignores the self-efficacy and anxiety construct although they were found significant.

5.2.

Service Quality

The Service Quality construct is defined as the overall support delivered by the service provider, and appliesregardless of whether this support is delivered by the IS department, a new organizational unit, or outsourced (DeloneandMcLean

2003). Support of users by the service provider is often measured by

the assurance, responsiveness,reliability, and empathy of the support organization (Petter&

McLean

2009). The inclusion of service quality in theupdated DeLone& McLean

(2003) model reflects IS functions or IS organizations rather than IS applications,

to reflectthe importance of service and support in successful information system (Iivari 2005, Wu&Wang 2006). It was addedbecause the changing nature of IS called for a measure to assess service quality when evaluating IS acceptance (Petter&McLean

2009). Lin et al. (2006) argued that system quality and information quality may be the most important qualitydimensions whereas service quality may be the most important factor for measuring the overall success of the ISdepartment. Therefore, service quality was not considered in their study, because their focus was to measure the successof ERP systems rather than the IS department. However, researchers believe that service quality is an important elementin information system success (Landrum&Prybutok 2004,Bienstock

et al. 2008). Although a claim could be made thatservice quality is merely a subset of the system quality, the changes in the role of IS over the last decade argue for aseparate variable (Delone& McLean

2003). Chien&Tsaur (2007) argued

that service quality needs to be included tomeasure service-level aspects since system quality focuses more on technology-level measures.Bienstock et al. (2008)found empirical evidence for a significant causal relationship between service quality and constructs related to users'satisfaction and intention to use.

5.3.

Level of Integration

Organizations perceive ERP as a vital tool for organizational competition as it integrates dispersed organizationalsystems and enables flawless transactions and production (Koh et al.2008). ERP vendors traditionally offered a singleERP system

et al. 2003,Tchokogue et al. 2005). However, although most companies still follow the single source approach, a significant numberof firms employ a strategy of “best of breed” ERP to maintain or create a competitive advantage (Shaul&Tauber, 2013).ERP vendors begun to acquired products or develop their own functionality that was either comparable or better thanmany of the "best of breed" applications, and hence enabled companies to maintain or create a competitive advantagebased on unique business processes, rather than adopting the same business processes which would leave no firm with anadvantage (Bradley 2008). In recent years, integration has prompted leading investments due to the functionality gap andthe need to extend andintegrate the ERP system to other enterprises or "best of breed" applications (Jacobson et al.2007). Integration was ranked as one of the leading investments for 2003, and well over 80% of U.S. companiesbudgeted for some type of integration in2002

and roughly one-third of U.S. companies defined application integrationas one of their top three IT investments in 2003 (Caruso 2003). ERP license revenue remained steady as companiescontinued their efforts to broadly deploy core applications and then added complementary functionalities in later phases.Today a greater effort is being made to integrate more mobile devices with the ERP system. ERP vendors are working toextend ERP to these devices along with users’ other business applications. The technicalstakes of the ERP concernintegration: this has involved hardware, applications, networking, supply chains and has covered more functions androles including decision making, stakeholders' relationships, standardization, transparency, globalization, etc.(Akkermans et al. 2003, Lim et al. 2005, Botta-Genoulaz et al. 2005).

5.4.

Offset from standard

An ERP system is radically different from traditional systems development (Dezdar&Sulaiman, 2009). ERPsystems are based on industry best practices, and are intended to be deployed

as is, thus offering organizationsconfiguration options that allow them to incorporate their own business rules. However, there are often functionality gapsremaining even after the configuration is complete between the best practices processes implemented within the ERPsystem and the organization's pre-implementation business processes, and organizations often suffer from poor fitbetween the ERP system and the organization. Organizations can avoid major misfits by applying two different strategiesto better match the delivered ERP functionality: technical customization such as rewriting part of the deliveredfunctionality within the ERP system, or interfacing to an external system, which is the most invasive, or finally businessprocess reengineering (Fryling 2010).

Customization potentially leads to more software process customization, more cycles of re-implementation and anincrease in testing activities, complexity, resources and a longer project schedule which can slow down the project andgenerating risky bugs in both present and in future maintenance. ERP vendors provide upgrades to guarantee support forthe system o 'fix' outstanding ‘bugs’, current bestpractices or design weaknesses (Agerfalk et al. 2009, Shaul&

Tauber2011).

To avoid ERP software modifications and its consequences many organizations are committed

to a “vanilla”implementation (Al-Mudimigh 2007, Finney&Corbett 2007). However, ERP vendors have a rather different view ofcustomization than the adopting organizations, in that most vendors consider customization to be an evolving process(Luo&

Strong 2004).

6.

Limitations

Regardless of the significance of the relationships between factors in the regression model, these relationships maynot apply to large enterprises since the respondents' experience relates to SMEs operating in thelocal market.SMEs,unlike LEs, face much greater constraints in terms of the resources that can be committed to all stages of informationgathering, although the complexity and amount of IT functionality and integration requirements are oftensimilar (Chanet al. 2012, Shaul & Tauber 2011). As a result, SMEs are forced to make implementation compromises according toresource constraints, which increase the risks inherent to

the implementation process (Sun et al. 2005). In additiondifferences in the scope of implementation in general as well as organizational, technological and environmental factorsmake it difficult to present a generalized perspective on implementation

(Koh & Saad 2006). Finally this study wasconducted with limited samples across different models and therefore, for practical analytical reasons, the authorsoperationalized each of the core constructs in the models by using the highest-loading items from each of the respectivescales as recommended by Nunnally&Bernstein (1994).

7.

Conclusion

The primary purpose of this paper was to synthesize the current state of the art with respect to users' symbolicadoption of information technologies in complex IT settings such as

ERPs. It reviewed the literature on the maininformation system acceptance models and their extensions, and empirically compared them as regards ERP systems.

Each of these models makes important and unique contributions to the literature on user acceptance of IT. It alsoexamined the effect of key moderators on these relationships (i.e. age, gender and experience) were also examined.

The findings are consistent with previous research in less complex IT settings, with regard to the interaction betweenkey moderators and core construct in complex IT settings such as ERPs. For instance, in implementing enterprisesystems such as ERP systems, PEOU was found to be a significant predictor of user symbolic adoption within eachmodel and less experienced users place more importance on ease of use r than experienced users as they tend to gainefficacy over time. However, the findings also show that complex IT settings are unique in a certain sense. Contrary toinitial hypotheses, and in contrast to previous studies, the influence of the perceived usefulness (defined in TAM, TAM2and TAM+TTF models), performance expectancy (defined in the UTAUT and CSE model) and relative advantage(defined in the DOI model) on user symbolic adoption of an ERP system is not moderated by age and gender but ratherby experience. In addition, these constructs were found to be unstable across the different studies, thus implying thatfurther examination is needed. Complex IT settings such as ERP systems are rich in functionalities beyond the needs ofthe average user. Therefore, users' main concern may be the extent to which the ERP system is easy to use rather than theextent to which the system is useful.

8.

Future research

The acceptance of complex information technology such as ERPs is still affected by intangibles; hence future workon adoption is critical. As shown in the review of the literature, recent efforts to

develop technology acceptance modelshave mostly focused on two dimensions: enriching or extending the model from theoretical perspectives and empiricallyfurther validating the performance of the models with various innovations in different environments.

Although studies have made great progress and the variance explained by several models are respectable in terms ofbehavioral research, further work should attempt to identify and test additional boundary conditions of the model toprovide an even richerunderstanding of technology adoption and usage behavior. In particular more attention should bepaid to investigating the influence of broad organizational, managerial, technological, operational and environmentalvariables. The influence of other moderating variables such as organization size, education level, orientation (e.g.technological, business), level of management, private vs. public sector and developing countries vs. developed countriesalso deserve work. A closer examination of the role moderating variables and their psychological and organizationalbasis could also shed light on their moderating role.